Big Data and its Unseen Impact.

&nbsp Increased access to information can do more harm than good. &nbsp This is because the more information is available, the easier it is for people to cherry-pick information that supports their pre-existing positions, or to perceive patterns where there are none.
&nbsp The invention of the printing press may have given rise to religious wars on account of facilitating the development of ideological agendas. &nbsp If you use or sell data you need to know&nbsp on and off-site repurcussions, final assessments, all of these things, that companies have failed to asses prior to embarking on any strategy. You may be asking yourself,&nbsp is my company helping me to prepare for any backlash from big data? The odds are, probably not. &nbsp Find out the various elements involved&nbsp when working with big data.

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information.
Some highlights from Nate Silver's "The Signal and the Noise"
21 JonahSinick 13 July 2013 03:21PM
As a part of my work for MIRI on the "Can we know what to do about AI?" project, I read Nate Silver's book The Signal and the Noise: Why So Many Predictions Fail — but Some Don't. I compiled a list of the takeaway points that I found most relevant to the project. I think that they might be of independent interest to the Less Wrong community, and so am posting them here.
Because I've paraphrased Silver rather than quoting him, and because the summary is long, there may be places where I've inadvertently misrepresented Silver. A reader who's especially interested in a point should check the original text.
Main Points
The deluge of data available in the modern world has exacerbated the problem of people perceiving patterns where none exist, and overfitting predictive models to past data.
Because of the risk of overfitting a model to past data, using a simple model can give more accurate results than using a refined model does.

A major reason that predictions fail is that predictors often don't take model uncertainty into account. Looking at a situation from multiple different angles can be a guard against failure to give adequate weight to model uncertainty.
Average different perspectives often yields better predictive results than using a single perspective.
Humans have a very strong tendency toward being overconfident when making predictions.
People make better predictions in domains where they have tight feedback loops to use to test their hypotheses.
Sometimes people's failure to make good predictions is the result of perverse incentives.
Introduction
Increased access to information can do more harm than good. This is because the more information is available,

the easier it is for people to cherry-pick information that supports their pre-existing positions, or to perceive patterns where there are none.
The invention of the printing press may have given rise to religious wars on account of facilitating the development of ideological agendas.

Pinpoint which of the book’s 100+ Understanding Big Data strategies will increase your staff decision making skills on big data.

About the Authors

— BIG DATA Skills training

The analysts know the flaws in the computer
models. These inevitably arise because—as a consequence of chaos theory—even the
most trivial bug in the model can have potentially profound effects. The unique
resource that these analysts were contributing was their eye­sight. It is a
valuable tool for analysts in any discipline—a visual inspection of a graphic
showing the interaction between two variables is often a quicker and more
reliable way to detect outliers in your data than a statistical test. It's also
one of those areas where computers lag well behind the human brain.Humans by contrast, out of pure
evolutionary necessity, have very powerful visual cortexes. They rapidly parse
through any distortions in the data in order to identify abstract qualities
like pattern and organization—qualities that happen to be very important in
different types of systems.
The deluge of data available in the modern world has exacerbated the problem of people perceiving patterns where none exist and overfitting predictive models to past data.
Because of the risk of and to past data, using a simple model can give more accurate results than using a refined model does.
A major reason that predictions fail is that predictors often don't take model uncertainty into account. Looking at a situation from multiple different angles can be a guard against failure to give adequate weight to model uncertainty.
Average different perspectives often yields better predictive results than using a single perspective.
Humans have a very strong tendency toward being overconfident when making predictions.
People make better predictions in domains where they have tight feedback loops to use to test their hypotheses.
Sometimes people's failure to make good predictions is the result of perverse incentives.

The Signal & Noise Big Data course delivers a step-by-step program for increasing
decision making skills while evaluating data strategies. Core perception skills
(those that get people into leadership positions) will sharpen
your core and adaptive leadership skills